Container escape attacks break isolation boundaries, granting threat actors code execution on the underlying host and potentially full control over the entire cluster. Existing runtime defenses exhibit an inherent trade-off. Anomaly- and provenance-based detection mechanisms achieve broad escape detection, yet incur substantial operational costs due to model retraining requirements or system-wide provenance capture. In contrast, industry rule-based detectors avoid these costs but offer limited detection coverage.

In order to maintain its presence on an infected system, malware employs a variety of persistence techniques. Although persistence is a well-known tactic of modern malware, our community lacks a comprehensive understanding of the types and prevalence of techniques adopted by Windows malware.

To preserve data privacy, multi-party computation (MPC) enables executing Machine Learning (ML) algorithms on private data. However, MPC frameworks do not include optimized operations on sparse data. This absence makes them unsuitable for ML applications involving sparse data; e.g., recommender systems or genomics. Even in plaintext, such applications involve high-dimensional sparse data, that cannot be processed without sparsity-related optimizations due to prohibitively large memory requirements.

Internet of Things (IoT) devices are ubiquitous, yet they often present security issues. The research community has invested substantial effort in designing automated methods for identifying these devices through passive network analysisan essential step in security applications such as anomaly detection, traffic monitoring, and vulnerability scanning. However, despite the promising results reported in laboratory settings, the effectiveness of these methods under realistic conditions remains unclear. In this work, we systematically review the existing literature on IoT device identification by studying the approaches, features, and evaluation environments. We then design and implement a framework to reproduce and evaluate selected identification methods. We re-implement the selected methods and assess their performance, using our framework, under realistic environmental factors, such as non-IoT traffic, dynamic user activity, and unknown devices. Our study reveals several important insights. We demonstrate that the performances of current identification methods significantly decline under realistic conditions. Furthermore, we highlight these methods' inability to differentiate between known and unknown devices, raising concerns about their effectiveness in security applications such as anomaly detection. We conclude by providing actionable recommendations for future research.

Abstract Having a strong password is vital in maintaining secure access to private or sensitive data. However, strong passwords require good memorization skills, placing a significant burden on human memory and cognitive capacity. Using additional authentication measures, such as token-based access, reduces the need for overly complex passwords while maintaining a high level of security. However, using additional measures introduces additional user interaction during the log-in process. In this work, we propose a password hardening scheme that provides a location based authentication mechanism. We use the information contained within the local WiFi environment to strengthen a user’s password. With our method, the requirements on the user password remain at a reasonable level, while keeping extra user involvement to a minimum. We achieve this by generating a cryptographic key from WiFi beacon frames, which we combine with the user password using a key derivation function. Furthermore, we conduct an analysis to assess the stability of local WiFi environments to determine the practicality of our proposed password hardening scheme.

Despite significant effort put into research and development of defense mechanisms, new malware is continuously developed rapidly, making it still one of the major threats on the Internet. For malware to be successful, it is in the developer’s best interest to evade detection as long as possible. One method in achieving this is using Code Injection, where malicious code is injected into another benign process, making it do something it was not intended to do. Automated detection and characterization of Code Injection is difficult. Many injection techniques depend solely on system calls that in isolation look benign and can easily be confused with other background system activity. There is therefore a need for models that can consider the context in which a single system event resides, such that relevant activity can be distinguished easily. In previous work, we conducted the first systematic study on code injection to gain more insights into the different techniques available to malware developers on the Windows platform. This paper extends this work by introducing and formalizing Behavior Nets: A novel, reusable, context-aware modeling language that expresses malicious software behavior in observable events and their general interdependence. This allows for matching on system calls, even if those system calls are typically used in a benign context. We evaluate Behavior Nets and experimentally confirm that introducing event context into behavioral signatures yields better results in characterizing malicious behavior than the state of the art. We conclude with valuable insights on how future malware research based on dynamic analysis should be conducted.

We present a WiFi signal-based, volatile key extraction system called RoomKey. We derive a room’s key by creating a deterministic key from the ever-changing WiFi environment and investigating the extraction capabilities of a designated area. RoomKey uses wireless beacon frames as a component, which we combine with a strong random key to generate and reconstruct the same volatile key in the room. We provide an exemplary use case using RoomKeyas an authentication factor using the location-specific WiFi environment as an authentication claim. We identified and solved two problems in using location as an authentication factor: location being sensitive to privacy and the location of a user constantly changing. We mitigate privacy concerns by recognizing a particular location without the need to localize its precise geographical coordinates. To overcome the problem of location change, we restrict locations to work environments for laptop usage and allow a per-location-predetermined, designated area (e.g., a room). With the concept RoomKey, we demonstrate the potential of including environmental WiFi measurements for volatile key extraction and show the possibility of creating location-aware and privacy-preserving authentication systems for continuous authentication and adaptive security measures.

Privacy-preserving biometric template protection schemes (BTPs) preserve biometric data by hiding biometric representations via a privacy-preserving mechanism (such as homomorphic encryption) and comparing the protected templates while conserving the recognition scores as in an embedding space. However, it is often tolerated to reveal these scores after performing a biometric comparison to gain efficiency and perform the score comparison directly on cleartext data. Through this work, we demonstrate that this cleartext score tolerance can lead to privacy breaches and bypass recognition systems, threatening those BTPs in the case of inner product-based facial template comparisons. We propose a template recovery attack that requires no training and a few random fake templates with their corresponding scores, from which we are able to recover the unprotected target template using the Lagrange multiplier optimization method. We evaluate our attack by verifying whether the recovered template is deemed similar to the target template held by recognition systems set to accept 0.1%, 0.01%, and 0.001% FMR. We estimate that between 60 to 165 revealed scores and fake templates can lead to a template recovery with a 100% success rate. We analyzed the impact of recovered templates by measuring the amount of gender information they contain, as well as their resemblance to the reconstructed images of their target templates.

Relying on Wi-Fi signals broadcasted by smartphones became the de-facto standard in the domain of pedestrian crowd monitoring. This method got the edge over other traditional means owing to the fact that insights are built upon data which uniquely identifies individuals and, thus, allows highly accurate crowd profiling over time. On the other hand, handling such uniquely identifying data in such a way that it does not expose the sensed individuals to potential privacy infringements proves to be a difficult task. Although several protection techniques were proposed, they yield data which, combined with other external knowledge, can still be used for tracing back to specific individuals. To address this issue, we propose a construction which protects the short-term storage and processing of privacy-sensitive Wi-Fi detections under strong cryptographic guarantees and makes available in the clear, as end results, only statistical counts of crowds. To produce these statistical counts, we make use of homomorphically encrypted Bloom filters as facilitators for oblivious set membership testing under encryption. We implement the system and perform evaluation on both simulated data and a real-world crowd-monitoring dataset, demonstrating that it is feasible to achieve highly accurate statistical counts in a privacy-friendly way.

In this paper we study verifiable sampling from probability distributions in the context of multi-party computation. This has various applications in randomized algorithms performed collaboratively by parties not trusting each other. One example is differentially private machine learning where noise should be drawn, typically from a Laplace or Gaussian distribution, and it is desirable that no party can bias this process. In particular, we propose algorithms to draw random numbers from uniform, Laplace, Gaussian and arbitrary probability distributions, and to verify honest execution of the protocols through zero-knowledge proofs. We propose protocols that result in one party knowing the drawn number and protocols that deliver the drawn random number as a shared secret.

The cutting-edge biometric recognition systems extract distinctive feature vectors of biometric samples using deep neural networks to measure the amount of (dis-)similarity between two biometric samples. Studies have shown that personal information (e.g., health condition, ethnicity, etc.) can be inferred, and biometric samples can be reconstructed from those feature vectors, making their protection an urgent necessity. State-of-the-art biometrics protection solutions are based on homomorphic encryption (HE) to perform recognition over encrypted feature vectors, hiding the features and their processing while releasing the outcome only. However, this comes at the cost of those solutions' efficiency due to the inefficiency of HE-based solutions with a large number of multiplications; for (dis-)similarity measures, this number is proportional to the vector's dimension. In this paper, we tackle the HE performance bottleneck by freeing the two common (dis-)similarity measures, the cosine similarity and the squared Euclidean distance, from multiplications. Assuming normalized feature vectors, our approach pre-computes and organizes those (dis-)similarity measures into lookup tables. This transforms their computation into simple table-lookups and summation only. We study quantization parameters for the values in the lookup tables and evaluate performances on both synthetic and facial feature vectors for which we achieve a recognition performance identical to the non-tabularized baseline systems. We then assess their efficiency under HE and record runtimes between 28.95ms and 59.35ms for the three security levels, demonstrating their enhanced speed.

Pedestrian dynamics are nowadays commonly analyzed by leveraging Wi-Fi signals sent by devices that people carry with them and captured by an infrastructure of Wi-Fi scanners. Emitting such signals is not a feature for devices of only passersby, but also for printers, smart TVs, and other devices that exhibit a stationary behavior over time, which eventually end up affecting pedestrian crowd measurements. In this paper we propose a system that accurately counts nonstationary devices sensed by scanners, separately from stationary devices, using no information other than the Wi-Fi signals captured by each scanner in isolation. As counting involves dealing with privacy-sensitive detections of people's devices, the system discards any data in the clear immediately after sensing, later working on encrypted data that it cannot decrypt in the process. The only information made available in the clear is the intended output, i.e. statistical counts of Wi-Fi devices. Our approach relies on an object, which we call comb, that maintains, under encryption, a representation of the frequency of occurrence of devices over time. Applying this comb on the detections made by a scanner enables the calculation of the separate counts. We implement the system and feed it with data from a large open-air festival, showing that accurate anonymized counting of nonstationary Wi-Fi devices is possible when dealing with real-world detections.

In this paper we propose and implement a digital permissioned decentralized anonymous payment scheme that finds a balance between anonymity and auditability. This approach allows banks to ensure that their clients are not participating in illegal financial transactions, whilst clients stay in control over their sensitive, personal information. Existing anonymous payment schemes often provide good privacy, but only little or mostly no auditability. We provide both by extending the Zerocash zk-SNARK based approach and adding functionality that allows for customer due diligence ‘at the gate’. Clients can do fully anonymous transactions up to a certain amount per time unit and larger transactions are forced to include verifiably encrypted transactions details that can only be opened by a select group of ‘judges’.

Security monitoring systems detect potentially malicious activities in IT infrastructures, by either looking for known signatures or for anomalous behaviors. Security operators investigate these events to determine whether they pose a threat to their organization. In many cases, a single event may be insufficient to determine whether certain activity is indeed malicious. Therefore, a security operator frequently needs to correlate multiple events to identify if they pose a real threat. Unfortunately, the vast number of events that need to be correlated often overload security operators, forcing them to ignore some events and, thereby, potentially miss attacks. This work studies how to automatically correlate security events and, thus, automate parts of the security operator workload. We design and evaluate DEEPCASE, a system that leverages the context around events to determine which events require further inspection. This approach reduces the number of events that need to be inspected. In addition, the context provides valuable insights into why certain events are classified as malicious. We show that our approach automatically filters 86.72% of the events and reduces the manual workload of security operators by 90.53%, while underestimating the risk of potential threats in less than 0.001% of cases.

Decentralized token exchanges allow for secure trading of tokens without a trusted third party. However, decentralization is mostly achieved at the expense of transaction privacy. For a fair exchange, transactions must remain private to hide the participants and volumes while maintaining the possibility for noninteractive execution of trades. In this paper we present a swap confidential transaction system (SwapCT) which is related to ring confidential transactions (e.g. used in Monero) but supports multiple token types to trade among and enables secure, partial transactions for noninteractive swaps. We prove that SwapCT is secure in a strict, formal model and present its efficient performance in a prototype implementation with logarithmic signature sizes for large anonymity sets. For our construction we design an aggregatable signature scheme which might be of independent interest. Our SwapCT system thereby enables a secure and private exchange for tokens without a trusted third party.

Biometric verification has been widely deployed in current authentication solutions as it proves the physical presence of individuals. Several solutions have been developed to protect the sensitive biometric data in such systems that provide security against honest-but-curious (a.k.a. semi-honest) attackers. However, in practice, attackers typically do not act honestly and multiple studies have shown severe biometric information leakage in such honest-but-curious solutions when considering dishonest, malicious attackers. In this paper, we propose a provably secure biometric verification protocol to withstand malicious attackers and prevent biometric data from any leakage. The proposed protocol is based on a homomorphically encrypted log likelihood-ratio (HELR) classifier that supports any biometric modality (e.g., face, fingerprint, dynamic signature, etc.) encoded as a fixed-length real-valued feature vector. The HELR classifier performs an accurate and fast biometric recognition. Furthermore, our protocol, which is secure against malicious adversaries, is designed from a protocol secure against semi-honest adversaries enhanced by zero-knowledge proofs. We evaluate both protocols for various security levels and record a sub-second speed (between 0.37s and 0.88s) for the protocol secure against semi-honest adversaries and between 0.95s and 2.50s for the protocol secure against malicious adversaries.

Measuring pedestrian dynamics using the signals sent from smartphones has become popular. Notably, Wi-Fi-based systems are currently widely deployed. However, many such systems have also become subject to serious debate due to privacy infringement. For some time, secure hashing of a smartphone’s unique MAC address was considered to be sufficient, yet this method has been overruled by Europe’s General Data Protection Regulation which states that an individual should not be identifiable from any dataset without explicit prior consent.

Abstract We propose a generic construction for fully secure decentralized multiauthority predicate encryption. In such multiauthority predicate encryption scheme, ciphertexts are associated with one or more predicates from various authorities and only if a user has a set of decryption keys that evaluates all predicates to true, the user is able to recover the message. In our decentralized system, anyone can create a new authority and issue decryption keys for their own predicates. We introduce the concept of a multi-authority admissible pair encoding scheme and, based on these encodings, we give a generic conversion algorithm that allows us to easily combine various predicate encryption schemes into a multi-authority predicate encryption variant. The resulting encryption schemes are proven fully secure under standard subgroup decision assumptions in the random oracle model. Finally, by instantiating several concrete multi-authority admissible pair encoding schemes and applying our conversion algorithm, we are able to create a variety of novel multi-authority predicate encryption schemes.

We present DECANTeR, a system to detect anomalous outbound HTTP communication, which passively extracts fingerprints for each application running on a monitored host. The goal of our system is to detect unknown malware and backdoor communication indicated by unknown fingerprints extracted from a host's network traffic. We evaluate a prototype with realistic data from an international organization and datasets composed of malicious traffic. We show that our system achieves a false positive rate of 0.9% for 441 monitored host machines, an average detection rate of 97.7%, and that it cannot be evaded by malware using simple evasion techniques such as using known browser user agent values. We compare our solution with DUMONT [24], the current state-of-the-art IDS which detects HTTP covert communication channels by focusing on benign HTTP traffic. The results show that DECANTeR outperforms DUMONT in terms of detection rate, false positive rate, and even evasion-resistance. Finally, DECANTeR detects 96.8% of information stealers in our dataset, which shows its potential to detect data exfiltration.

Aggregation of time-series data offers the possibility to learn certain statistics over data periodically uploaded by different sources. In case of privacy sensitive data, it is desired to hide every data provider's individual values from the other participants (including the data aggregator). Existing privacy preserving time-series data aggregation schemes focus on the sum as aggregation means, since it is the most essential statistics used in many applications such as smart metering, participatory sensing, or appointment scheduling. However, all existing schemes have an important drawback: they do not provide verifiable outputs, thus users have to trust the data aggregator that it does not output fake values. We propose a publicly verifiable data aggregation scheme for privacy preserving time-series data summation. We prove its security and verifiability under the XDH assumption and a widely used, strong variant of the Co-CDH assumption. Moreover, our scheme offers low computation complexity on the users' side, which is essential in many applications.

Vehicular ad-hoc networks are a promising research area. Besides improving safety, traffic efficiency enhancements are a major expected benefit. In this paper, we present a novel security mechanism for traffic efficiency applications that leverages on velocity-based vehicle clustering and uses HyperLogLog estimators to create bandwidth-efficient integrity proofs. Evaluation results show that our mechanism achieves high protection against plausible attacker models, and that it is more bandwidth efficient than a comparably secure security mechanism that does not employ clustering.

Forensic image recognition tools are used by law enforcement agencies all over the world to automatically detect illegal images on confiscated equipment. This detection is commonly done with the help of a strictly confidential database consisting of hash values of known illegal images. To detect and mitigate the distribution of illegal images, for instance in network traffic of companies or Internet service providers, it is desirable to outsource the recognition of illegal images to these companies. However, law enforcement agencies want to keep their hash databases secret at all costs as an unwanted release may result in misuse which could ultimately render these databases useless. We present SOFIR, a tool for the Secure Outsourcing of Forensic Image Recognition allowing companies and law enforcement agencies to jointly detect illegal network traffic at its source, thus facilitating immediate regulatory actions. SOFIR cryptographically hides the hash database from the involved companies. At fixed intervals, SOFIR sends out an encrypted report to the law enforcement agency that only contains the number of found illegal images in the given interval, while otherwise keeping the company's legal network traffic private. Our experimental results show the effectiveness and practicality of our approach in the real-world.

Searchable Symmetric Encryption (SSE) allows a client to store encrypted data on a storage provider in such a way, that the client is able to search and retrieve the data selectively without the storage provider learning the contents of the data or the words being searched for. Practical SSE schemes usually leak (sensitive) information during or after a query (e.g., the search pattern). Secure schemes on the other hand are not practical, namely they are neither efficient in the computational search complexity, nor scalable with large data sets. To achieve efficiency and security at the same time, we introduce the concept of distributed SSE (DSSE), which uses a query proxy in addition to the storage provider. We give a construction that combines an inverted index approach (for efficiency) with scrambling functions used in private information retrieval (PIR) (for security). The proposed scheme, which is entirely based on XOR operations and pseudo-random functions, is efficient and does not leak the search pattern. For instance, a secure search in an index over one million documents and 500 keywords is executed in less than 1 second.

Secure multiparty computation enables a set of users to evaluate certain functionalities on their respective inputs while keeping these inputs encrypted throughout the computation. In many applications, however, outsourcing these computations to an untrusted server is desirable, so that the server can perform the computation on behalf of the users. Unfortunately, existing solutions are either inefficient, rely heavily on user interaction, or require the inputs to be encrypted under the same public key - drawbacks making the employment in practice very limited. We propose a novel technique based on additively homomorphic encryption that avoids all these drawbacks. This method is efficient, requires no user interaction whatsoever (except for data upload and download), and allows evaluating any dynamically chosen function on inputs encrypted under different public keys. Our solution assumes the existence of two non-colluding but untrusted servers that jointly perform the computation by means of a cryptographic protocol. This protocol is proven to be secure in the semi-honest model. By developing application-tailored variants of our approach, we demonstrate its versatility and apply it in two real-world scenarios from different domains, privacy-preserving face recognition and private smart metering. We also give a proof-of-concept implementation to highlight its feasibility.